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Cooperative Binding of Transcription Regulators

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome. Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...

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A self-attention model for inferring cooperativity between regulatory features.

Fahad Ullah¹, Asa Ben-Hur¹

¹Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA.

Nucleic Acids Research

|May 5, 2021

Summary

We developed SATORI, a deep learning model using self-attention to identify interactions between regulatory elements. SATORI accurately detects transcription factor-transcription factor interactions, improving upon existing methods.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Deep learning models excel at predicting biological processes like gene expression.
Extracting biologically relevant insights from these models is crucial but challenging.
Identifying cooperativity between regulatory elements, key to gene expression, remains an open problem.

Purpose of the Study:

To introduce SATORI, a novel self-attention-based model for detecting regulatory element interactions.
To capture global interactions between regulatory elements within a sequence.
To enhance the interpretability of deep learning models in genomics.

Main Methods:

Utilized a combination of convolutional layers and a self-attention mechanism.
Developed a self-attention-based model named SATORI (Self-ATtentiOn based model to detect Regulatory element Interactions).
Focused on detecting transcription factor-transcription factor (TF-TF) interactions.

Main Results:

SATORI successfully identified numerous statistically significant TF-TF interactions, many previously documented.
The model demonstrated superior performance in detecting experimentally verified TF-TF interactions compared to existing methods.
SATORI does not require computationally intensive post-processing steps.

Conclusions:

SATORI offers an effective and efficient approach for detecting regulatory element interactions, particularly TF-TF interactions.
The model's self-attention mechanism facilitates a comprehensive understanding of element cooperativity.
The SATORI framework is adaptable for detecting various feature interactions in attention-based models.